143 results about "Data optimization" patented technology

The invention provides a robot training data analysis method and system based on deep learning. The method comprises the steps that operation parameters reflecting the work intensity of a robot are acquired; acquiring original sensor data acquired by the robot; deducing the physical state deviation of an end effector of the robot based on the operation parameters; according to the physical state deviation, adjusting the original sensor data to obtain adjusted sensor data; and optimizing a robot control strategy based on the adjusted sensor data. The method can effectively identify and quantify the sensor data error of the robot caused by the physical state deviation, and solves the problem that the model performance is reduced due to the fact that a traditional deep learning system learns in dirty data by correcting the original sensor data, thereby improving the accuracy and efficiency of robot control strategy optimization, and improving the user experience. And invalid adjustment caused by error attribution is avoided.

The application provides a kind of IGBT junction temperature prediction model construction method and system, it is related to power electronic device thermal management technical field, method includes: obtaining the initial data set of the transient thermal response relationship between the IGBT chip and the NTC temperature measuring point in the IGBT power module;Based on the initial data set, the neural network model is trained, and the nonlinear function relationship between time and thermal impedance is learned and characterized;The preset new time series is input into the trained neural network model, and the optimized thermal impedance data set with continuous time and noise suppression is generated;Based on the data set, the lumped parameter thermal network model is parameter fitted, to identify its thermal resistance and heat capacity parameters, complete the construction of junction temperature prediction model.The application uses neural network as a data optimization tool in the parameter identification process of physical model, only uses the temperature signal of NTC temperature measuring point provided by the module, realizes the accurate, convenient online prediction of IGBT junction temperature.

This invention relates to the field of artificial intelligence technology, addressing the technical problems of existing technologies that easily lead to information fragmentation, lack of complex spatiotemporal dependencies, and lack of sensitivity to dynamic data changes, resulting in bandwidth waste or loss of critical information. Specifically, it relates to a method and system for real-time synchronization processing of interactive data from intelligent devices. This method includes the following steps: deploying various sensors and monitoring devices on campus, collecting raw sensor data and monitoring video data, and preprocessing the raw sensor data and monitoring video data to obtain a standardized dataset. This invention integrates multimodal data by constructing a spatiotemporal heterogeneous graph, significantly reducing redundant data transmission, achieving efficient edge-cloud collaborative computing under low bandwidth, improving deep coupling and dynamic data optimization, and enhancing the real-time performance of campus security and the monitoring efficiency of sensitive campus scenarios.

A metal modified graphene structure model is applied to predict hydrogen storage materials, comprising a structure simulation unit, a simulation data unit, a preparation measurement unit, a data processing unit and a parameter feedback unit; the structure simulation unit includes a graphene base structure composed of carbon atoms and a plurality of modified structure models pre-prepared for metal modification of the graphene base, combined into hydrogen storage materials; the simulation data unit calculates the structure of H2 molecules adsorbed on the hydrogen storage materials and calculates the simulation hydrogen storage data; the preparation measurement unit actually prepares the hydrogen storage materials and measures the actual hydrogen storage data; when the actual hydrogen storage rate data is greater than or equal to the first threshold value, the data processing unit calculates the difference percentage of the actual hydrogen storage data and the simulation hydrogen storage data, and outputs the data difference degree; otherwise, an abnormal warning signal is output; when the data difference degree is less than or equal to the second threshold value, the parameter feedback unit outputs that the hydrogen storage data of the preparation measurement unit is qualified, otherwise, an abnormal warning signal is output and feedback is performed, and data optimization is performed.

This invention belongs to the technical field of environmental multi-parameter monitoring methods, and particularly relates to an environmental multi-parameter monitoring method and system for optimizing and denoising island and reef data. Based on prior knowledge of island and reef environmental physics, an unsupervised pseudo-label generation system is constructed. A multi-parameter numerical simulation model is built based on tidal cycles, temperature-salinity-density coupling, and the physicochemical equilibrium law of dissolved oxygen-temperature-pH. Environmental time-series characteristics of measured noise data are input to generate clean simulation reference data. A self-supervised learning framework is used to pre-train the basic denoising model with the clean simulation data as a weakly supervised signal. A physics-driven multi-parameter coupling and noise decoupling mechanism is constructed, transforming the physicochemical coupling relationships of temperature, salinity, pH, and dissolved oxygen into hard constraints for the model and establishing a parameter coupling correlation matrix. Through feature decoupling branches, environmental coupling change components and noise components are separated, distinguishing between parameter coupling pseudo-noise and real noise from sensor drift and transmission interference. Denoising bias of a single parameter is corrected through coupling constraints.

This invention relates to the field of spacecraft orbit control technology, and particularly to a spacecraft approach control method based on the fusion of imitation learning and reinforcement learning. The method includes: S1: generating initial and target states, solving for the optimal fuel transfer trajectory offline using a direct method, and constructing an expert dataset; S2: based on the expert dataset, training a neural network controller through imitation learning to obtain a pre-trained model; S3: loading the weights of the pre-trained model, constructing a composite reward function, and training the policy network using a near-end policy optimization algorithm to obtain an optimized policy network model; S4: acquiring relative state data, optimizing the policy network model, and outputting the control quantity for the optimal fuel consumption trajectory transfer in real time. This invention rapidly masters an approximately optimal policy through imitation learning and surpasses the performance of expert data through reinforcement learning, completing control command solutions in milliseconds. It balances terminal control accuracy and fuel consumption optimization, offering advantages such as high solution efficiency, excellent control accuracy, and strong generalization ability.

This application discloses a call method, apparatus, electronic device, and storage medium. This application embodiment can collect its own raw call data; after joining a short-range communication network, it acquires the raw call data of secondary terminals through the short-range communication network. All terminals in the short-range communication network are within the signal coverage range of the network, which includes one primary terminal and at least one secondary terminal. The raw call data of all terminals in the short-range communication network is optimized to obtain optimized call data. The optimized call data is sent to a server, and a call data set returned by the server is obtained, including the optimized call data from the short-range communication network. The call data set is then sent to each secondary terminal through the short-range communication network. Therefore, this solution can improve call quality.

This application discloses a semantic map construction method, apparatus, electronic device, and storage medium. The method includes: acquiring source data for the semantic map, including image data, laser data, inertial navigation data, and satellite positioning data; determining the vehicle pose corresponding to the image data based on the image data and inertial navigation data, and determining the vehicle pose corresponding to the laser data based on the laser data and inertial navigation data; constructing a first nonlinear optimization model based on the vehicle pose corresponding to the image data, the vehicle pose corresponding to the laser data, and the satellite positioning data to determine the optimized vehicle pose; determining the three-dimensional semantic information corresponding to the image data based on the image data and laser data; and constructing a semantic map based on the optimized vehicle pose and the three-dimensional semantic information. This application optimizes the vehicle pose by combining data from multiple sensors, improving the accuracy of semantic map construction, and improves the efficiency of semantic map construction by combining image data and laser data.

The application discloses an AI driving-based structured data migration method and system, belongs to the technical field of big data migration, and aims to solve the technical problem of how to improve the efficiency of large-scale data migration and reduce resource waste. The method comprises the following steps: forming a private large language model with field adaptation capability; performing lexical analysis and syntax analysis on non-standard structured data in a source database based on an AST analysis method, generating an abstract syntax tree AST, traversing and optimizing AST nodes, and converting the AST nodes into standard structured data supported by a target database; calling the large language model, and converting unstructured data into structured data supported by the target database through instruction fine-tuning, thought chain reasoning and step-by-step analysis; generating a migration script by the large language model; monitoring the migration process in real time, recording abnormal events, automatically retrying or repairing the abnormal events by the large model, and optimizing the large language model configuration strategy and data conversion rules in combination with feedback data.

The application discloses a satellite data optimization method and system based on two-stage decision, and a processing system, belongs to the technical field of remote sensing satellite data processing, and solves the technical problems that the existing technology has low manual selection efficiency and cannot select optimal data. The application obtains a map selected range vector and a satellite standard scene to be selected, attaches a single scene contribution area of each satellite standard scene to be selected and a sorting value field of a pre-computed vector spliced based on priority to an attribute table of the corresponding satellite standard scene to be selected, performs twice sorting on the satellite standard scene to be selected, adopts a first-stage global coverage decision and a second-stage local optimal decision, and obtains optimal satellite data. The application is used for selecting optimal data from selected data on the basis of unchanged coverage area, minimizing delivery area, and greatly improving the selection efficiency of users and data processing personnel.

The application provides a collaborative case-packing global optimization method and system, and belongs to the technical field of data optimization. Specifically, a modeling module is responsible for generating all in-box allocation schemes that meet the requirement of "at least one piece of SKU is allocated", a scoring module is responsible for calculating a candidate scheme set by using a small root heap to dynamically maintain the top K candidate schemes with the highest scores, controlling the size of the candidate schemes, and a solving module is responsible for selecting an optimization scheme with the minimum complexity level that can minimize the overall remaining SKU demand as the final output scheme. On the basis of reducing transportation costs, the efficiency of identifying and processing the optimal collaborative case-packing scheme is improved.

The application discloses a PCB intelligent manufacturing quality prevention and diagnosis method, comprising the following steps: collecting and structuring defect data of key processes in the whole PCB manufacturing process to form a knowledge base; collecting real-time production data corresponding to the associated parameters in the knowledge base; based on the collected real-time production data, using a pre-trained defect prediction model or / and a rule engine to perform risk prediction and early warning; when defects are actually detected by online detection equipment or the system triggers a risk warning, intelligent diagnosis of defect root causes and improvement measures are recommended; a feedback mechanism is established to record and evaluate the diagnosis results and improvement measures each time, and feedback to the knowledge base, defect prediction model and rule engine for data optimization and updating. The PCB intelligent manufacturing quality prevention and diagnosis method can realize intelligent prediction, rapid diagnosis and measure recommendation of defects, and significantly improve the quality stability and problem solving efficiency. The application also provides a PCB intelligent manufacturing quality prevention and diagnosis system.

The application relates to the technical field of heater detection, and discloses an automatic air tightness and insulation voltage resistance detection method for a high-pressure heater product, which comprises the following steps: step S1 product association and parameter calling, step S2 composite air tightness detection, step S3 graded insulation voltage resistance detection, step S4 construction of a traceable data link, and step S5 product shunting and feedback data optimization process. The two-stage composite air tightness detection and the graded insulation voltage resistance detection are cooperatively designed, the data of a production management system and a detection system are synchronized, a complete traceable data link which is deeply bound by a product, detection and a production process is constructed, the detection precision and efficiency are cooperatively improved, the nature of product defects is accurately positioned and determined, the causes of defects are quickly traced, a precise data basis is built for production process optimization, and the problems that detection and production data are split and defect tracing is difficult in the prior art are effectively solved.

This invention discloses a method for constructing a virtual psychological supervisor based on multi-agent collaboration, belonging to the fields of digital mental health training and artificial intelligence technology. By constructing a multi-agent collaborative framework of "error-prone counselor agent - error-sensitive patient agent - error-correcting supervisor agent," and combining iterative optimization mechanisms guided by validators, high-quality "consultation dialogue - supervision feedback" aligned data pairs are generated. Based on data optimization of a large language model, a virtual psychological supervisor with error recognition, professional feedback generation, and skills assessment capabilities is ultimately constructed. This invention solves the problems of insufficient training data, inadequate feedback professionalism, and subjective assessment standards in traditional psychological counseling supervision, achieving safe, efficient, standardized, and scalable psychological counselor skills supervision, significantly improving supervision accuracy and training efficiency.

The present application relates to the technical field of power system metering, in particular to a method and system for operation error diagnosis and analysis of electric energy meter based on electric energy meter operation error, wherein the method comprises: obtaining electric energy meter measurement value curve data of a target area in a set period from a metering automation system, and generating standardized electric energy meter operation data sequence through preprocessing; using an improved spectral clustering algorithm to carry out curve shape clustering on the data sequence, combining elbow rule to determine the optimal classification number of electric energy meter operation state, and completing the classification of electric energy meters in different operation states; relying on the principle of energy conservation to build a state evaluation model, screening effective data points through a data optimization strategy, reducing the model ill-conditioning by means of an improved Tikhonov regularization method, solving the error estimation equation to obtain the electric energy meter operation error estimation value and complete the state diagnosis. This method is suitable for processing massive electric energy meter time series data, smoothly weakening model operation interference, regularizing the electric energy meter operation error solving process, and optimizing the working condition diagnosis adaptability.

This invention discloses a cost data optimization method and system based on a flexible task work order mechanism, belonging to the field of work order allocation technology. The system includes a dynamic sensing module, an efficiency analysis module, a cost optimization module, and a data storage module. The dynamic sensing module receives task work orders from clients and collects task records from all service objects within the park. The efficiency analysis module analyzes all work order records completed by each service object in each cycle based on the task records, calculates the cost index, and fits the efficiency index relationship by combining the interval duration of the work order records. The cost optimization module calculates the cost index of each task work order, sorts them, and performs the first round of allocation to service objects. Based on the efficiency index relationship, it predicts the efficiency index changes of each service object, determines the interval duration, and iteratively allocates task work orders. The data storage module generates a task list for each service object, records the process information of executing task work orders, and saves it to the task record.

The application relates to the technical field of data retrieval and fraud detection, and discloses a vehicle insurance data optimization retrieval method for a digital management platform, which comprises the following steps: acquiring vehicle insurance data, performing multi-view feature extraction, and constructing a unified feature space representation; generating high-quality fraud samples by using a conditional generative adversarial network to enhance model training; constructing a deep reconstruction network to amplify abnormal feature signals through reconstruction error; constructing a difference-sensitive hash index structure based on the features of the reconstruction error; comprehensively evaluating the retrieved suspected fraud samples by using a multi-view consistency verification method to output final fraud detection results; and generating high-quality fraud samples by using the conditional generative adversarial network, so that the problem of data imbalance caused by the scarcity of fraud samples is effectively solved, and the model generalization capability is improved.

The application relates to the technical field of image recognition, and particularly discloses a leather product comparison and identification method and system based on image recognition. The application extracts a multi-dimensional standardized feature vector reflecting inherent physical and mechanical properties by collecting dynamic deformation videos of a to-be-inspected leather and a reference sample under controlled micro force, receives identification task description parameters, maps core discriminant features from a physical and mechanical feature knowledge base, dynamically instantiates a self-adaptive identification model through a meta-learning model, generates a feature weighting scheme and a dynamic decision threshold, calculates a mechanical feature matching degree and generates a visual preliminary report, adjusts the scheme in combination with user interactive correction instructions, updates the result and feeds back data optimization meta-models. The application upgrades the identification basis to essential mechanical properties, realizes task self-adaptive decision and man-machine collaborative optimization, can improve identification reliability and scene adaptability, makes the decision process transparent and interpretable, has a continuous optimization capability, and is suitable for multi-class requirements such as authenticity identification and traceability.

This invention relates to a data optimization method for a foundry monitoring system, comprising a monitoring center and a data optimization module. The data optimization module utilizes a comprehensive, end-to-end optimization approach covering data acquisition, preprocessing, transmission, storage, and display to reduce data redundancy. The data optimization module is electrically connected to the monitoring center's cache module, data extraction module, and data conversion module, and also communicates bidirectionally with the human-machine interface display screen. The end-to-end optimization approach specifically includes: reducing the amount of source data through differentiated sampling strategies; streamlining invalid data through multi-level dynamic filtering; balancing real-time performance and bandwidth usage through a tiered transmission mechanism; separating hot and cold data storage to avoid storage accumulation; and finally, reducing hardware load through dynamic refresh at the display end. This invention, through its end-to-end optimization scheme, reduces data redundancy at the source, streamlines invalid information during transmission and processing, and matches data characteristics during storage and display.

This invention relates to the field of intelligent education technology, specifically to a method and system for guiding student device self-management based on achievement incentives. The method includes: performing differential privacy perturbation processing on raw data of student device usage behavior to form aggregated statistical behavior data; associating and mapping the aggregated statistical behavior data with a preset educational scenario template to generate an initial control strategy for student devices; deploying a process protection component according to the initial control strategy to generate process protection status data; merging the process protection status data and the aggregated statistical behavior data, and generating a self-management badge and matching corresponding self-management permissions when the behavior performance evaluation meets the continuous achievement conditions; after a student's temporary time extension request initiated by the permission is approved, optimizing the control strategy by coupling peripheral device status data, synchronizing it to the local policy engine, and supporting the independent adjustment of some parameters; this invention can improve the efficiency of student device self-management.

This invention relates to the field of energy management technology, and in particular to a method and apparatus for energy management and adaptive power consumption control of unmanned systems. The method acquires multi-source operating data and real-time load location, extracts target energy operating data, collects dynamic power consumption fluctuation data for the next cycle based on the initial power consumption allocation scheme, and optimizes and generates an updated power consumption scheme based on the target data to achieve dynamic adaptation. Based on the current energy storage status, target data, and load priority, it calculates the extreme power supply range and determines the target power-protected load set to ensure priority power supply to critical loads. When it is determined that the system is about to run out of energy, it intelligently generates a target load-cutting sequence by comprehensively considering real-time operating conditions, priorities, and energy storage status, and executes orderly graded power outages, improving energy utilization efficiency. Under extreme operating conditions, it can scientifically plan power protection and load-cutting strategies, effectively avoiding disorderly power outages, and significantly enhancing the power supply reliability, safety, and intelligence level of unmanned energy systems.

This invention discloses an intelligent data analysis method for LNG facilities that integrates multi-dimensional data and dynamic POI data. The method includes: constructing an LNG facility thesaurus; obtaining and organizing a list of core LNG facilities to obtain a preliminary LNG facility database; using a big data model to obtain LNG facility data and LNG electronic waybill data to supplement and optimize the preliminary LNG facility database, resulting in a final LNG facility database; a dynamic attribute update module using a dynamic dataset to dynamically adjust the operational status and activity level attribute information in the final LNG facility database; and a multi-scenario in-depth analysis module for LNG facility statistical display, supply and demand forecasting, and scheduling matching planning. This invention can obtain a comprehensive, accurate, timely, and dynamically attributed final LNG facility database, providing comprehensive and accurate data support for subsequent data analysis in the multi-scenario in-depth analysis module, and realizing in-depth analysis and processing of LNG facilities such as monitoring, supply and demand forecasting, and scheduling.

The application relates to the technical field of intelligent transportation and carbon governance, in particular to a passenger car carbon emission monitoring and regulation system and method based on multi-source spatio-temporal data. The application solves the problem of scattered and inconsistent data by preprocessing and uniformly mapping multi-source spatio-temporal data. A three-dimensional carbon emission calculation model of vehicle-space unit-time period is constructed to realize accurate calculation of single-trip carbon emission of a single vehicle and positioning of high-carbon emission hotspots. A clustering algorithm is used to complete differentiated division of user groups. A dynamic early warning threshold is generated based on a warning threshold prediction model, regional carbon emission targets and historical data to reduce false positives and false negatives. A warning type-policy tool-adaptation object mapping rule library is established, and a regulation strategy is generated by combining early warning signals and real-time data to realize automatic and accurate generation of regulation strategies from problem identification. The model parameter, early warning threshold and mapping rule are optimized by using policy implementation effect data to form a monitoring, early warning, regulation, optimization closed-loop management and realize accurate regulation.

The application provides a digital human interaction method and system based on gesture and body action recognition, and relates to the technical field of digital human interaction. The application collects user images and depth information, extracts skeletal key points of hands, whole bodies and heads and converts them into key point sequences containing time motion tracks. Then, a space-time graph structure is constructed based on the sequences, time-space features are extracted by a time sequence graph convolution network to identify gesture and body action categories. Subsequently, user intentions are inferred by combining interactive scene contexts and historical data, and interactive instructions are generated. Finally, voice, expression and action data are generated according to the instructions, action data is optimized, and driving signals are generated by synchronizing the three types of data, so that the digital human completes the interactive response, and precise and natural gesture and body interaction between the digital human and the user can be realized.

The application discloses a stealth structure multi-objective optimization method based on transfer learning and domain adaptation, which comprises the following steps: mapping stealth structure parameters to a unified feature space, and constructing a cross-domain parameter space model; training a domain adaptation transfer network model based on the cross-domain parameter space model; the domain adaptation transfer network model comprises a double-channel transfer module, which aligns the performance distribution of a source domain and a target domain through adversarial learning; optimizing the parameters of the trained domain adaptation transfer network model based on actual measurement data of the target domain; and obtaining an optimized stealth structure parameter combination which takes into account the stealth performance and mechanical performance based on the optimized domain adaptation transfer network model. The application further discloses a stealth structure multi-objective optimization system based on transfer learning and domain adaptation for implementing the above method. The application can realize efficient optimization of stealth structure parameters across scenes, and can be widely applied to the field of electromagnetic compatibility devices and the like, and has the advantages of high optimization efficiency and strong engineering applicability.

This invention discloses a method and system for monitoring highway pavement quality based on deep learning. The method includes raw image acquisition, data optimization processing, deep incomplete model construction, pavement monitoring model construction, and highway pavement quality monitoring. This invention obtains raw data through image acquisition; employs data optimization processing methods such as spatiotemporal alignment, region of interest extraction, sequence frame organization, data augmentation, and dataset partitioning; utilizes a method to generate dense depth maps from 2D image data and sparse depth maps for subsequent processing, acquiring high-density pavement geometric information while ensuring economy, thus providing crucial 3D morphological support for precise identification of pavement conditions; and employs a two-stream network model as the pavement monitoring model, processing image texture and geometric depth information separately and achieving dynamic fusion, while introducing uncertainty information from the depth incomplete stage, thereby improving the comprehensiveness and accuracy of the monitoring results.

The application discloses a kind of quality intelligent evaluation methods and systems for multi-source medical data, it is related to big data processing technical field.The quality intelligent evaluation methods and systems for multi-source medical data, including S1, acquisition multi-source medical data is preprocessed, store and construct medical database;S2, drive the spatiotemporal semantic alignment and joint representation learning of cross-modal data, construct medical data optimization model and output spatiotemporal alignment value;S3, fusion spatiotemporal correlation and multidimensional quality index, data quality evaluation and abnormal analysis are carried out;S4, according to data credible result executes the traceability path generation and explainability report output operation;S5, through user feedback data and model evolution state analysis optimization effect.It solves the problem that multi-source medical data exists in spatiotemporal alignment, quality evaluation and abnormal detection, spatiotemporal mismatch, data inconsistency and low quality repair efficiency.

The application discloses a coating film intelligent control system and control method for self-luminous road marking, relates to the technical field of road engineering, constructs a road marking, environment and traffic digital twin model, runs a deep reinforcement learning algorithm on an edge side, outputs optimal luminous parameter decisions through the digital twin model based on key characteristic parameters and traffic prediction data, forms a preliminary flexible controllable luminous unit control instruction, constructs a vehicle-road cloud integrated collaborative interaction mechanism, links vehicles, traffic platforms and cross-department systems, supplements real-time interaction data for algorithm decisions, optimizes flexible controllable luminous unit control instructions and outputs them. The application realizes multi-dimensional regulation and control of luminous parameters, enhances environmental and traffic adaptability, and an adaptive algorithm driven by a digital twin considers visible distance and energy consumption optimization, so that the energy consumption is greatly reduced while ensuring that the visible distance is greater than or equal to a set threshold value; the vehicle-road cloud integrated collaborative interaction mechanism greatly reduces response delay in emergency scenarios and improves road safety.

The present application relates to the technical field of electrical system design, and more particularly to a spacecraft electrical system interconnection component principle data optimization method and system. The method comprises the following steps: obtaining interconnection component data; structurally deconstructing the interconnection component data to obtain principle unit data; constructing cross-task inheritance paths and extracting assumption constraints based on the principle unit data to obtain cross-task inheritance path data and assumption constraint data, respectively; performing cross-generation principle consistency verification based on the cross-task inheritance path data and the assumption constraint data to obtain distortion unit data; and performing distortion accumulation evaluation on the distortion unit data to obtain distortion accumulation data to assist interconnection component optimization work. By constructing a cross-task inheritance path and principle assumption bearing mapping relationship, the present application can effectively identify implicit principle faults in the electrical system caused by structural pruning or inheritance distortion, and improve the reuse reliability of principle data in multiple generations of space missions.